U.S. patent number 10,303,866 [Application Number 16/141,084] was granted by the patent office on 2019-05-28 for automatic retries for facial recognition.
This patent grant is currently assigned to Apple Inc.. The grantee listed for this patent is Apple Inc.. Invention is credited to Thorsten Gernoth, Kelsey Y. Ho, Marcel Van Os.
United States Patent |
10,303,866 |
Van Os , et al. |
May 28, 2019 |
Automatic retries for facial recognition
Abstract
An operation of a facial recognition authentication process may
fail to authenticate a user even if the user is an authorized user
of the device. In such cases, the facial recognition authentication
process may automatically re-initiate to provide another attempt to
authenticate the user using additional captured images. For the new
attempt (e.g., the retry) to authenticate the user, one or more
criteria for the images used in the facial recognition
authentication process may be adjusted. For example, criteria for
distance between the camera and the user's face and/or occlusion of
the user's face in the images may be adjusted before the new
attempt to authenticate the user. Adjustment of these criteria may
increase the likelihood that the authorized user will be
successfully authenticated in the new attempt.
Inventors: |
Van Os; Marcel (Santa Cruz,
CA), Gernoth; Thorsten (San Francisco, CA), Ho; Kelsey
Y. (Los Altos, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Apple Inc. |
Cupertino |
CA |
US |
|
|
Assignee: |
Apple Inc. (Cupertino,
CA)
|
Family
ID: |
66636186 |
Appl.
No.: |
16/141,084 |
Filed: |
September 25, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
|
62679847 |
Jun 3, 2018 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K
9/00281 (20130101); G06F 21/32 (20130101); G06K
9/00912 (20130101); G06K 9/00288 (20130101); G06K
9/00268 (20130101); G06K 9/00255 (20130101) |
Current International
Class: |
G06K
9/00 (20060101); G06F 21/32 (20130101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
US. Appl. No. 15/881,261, filed Jan. 26, 2018, all pages. cited by
applicant.
|
Primary Examiner: Traore; Fatoumata
Attorney, Agent or Firm: Meyertons, Hood, Kivlin, Kowert
& Goetzel, P.C. Sampson; Gareth M.
Parent Case Text
PRIORITY CLAIM
This patent claims priority to U.S. Provisional Patent Application
No. 62/679,847 to Van Os et al., entitled "AUTOMATIC RETRIES FOR
FACIAL RECOGNITION", filed Jun. 3, 2018, which is incorporated by
reference in their entirety.
Claims
What is claimed is:
1. A method, comprising: receiving, on a user interface associated
with a device comprising a computer processor and a memory, an
unlock request for the device from a user; in response to receiving
the unlock request, obtaining at least one first image of the user
using a camera located on the device; assessing the at least one
first image to determine if a face of the user in the at least one
first image meets one or more selected criteria; in response to
determining that the face of the user in the at least one first
image meets the one or more selected criteria, obtaining one or
more second images of the face of the user using the camera located
on the device; assessing the second images using a facial
recognition authentication process to determine if the user is an
authorized user of the device, wherein the facial recognition
authentication process comprises: encoding the second images to
generate at least one feature vector, wherein the feature vector
represents one or more facial features of the user in the second
images; comparing the feature vector to one or more reference
templates stored in the memory of the device to obtain a matching
score; and authorizing the user to perform at least one operation
on the device that requires authentication if the matching score is
above an unlock threshold; in response to the user being determined
to not be an authorized user of the device by the facial
recognition authentication process: adjusting at least one of the
selected criteria; obtaining at least one third image of the user
using the camera located on the device; assessing the at least one
third image to determine if the face of the user in the at least
one third image meets the adjusted selected criteria; in response
to determining that the face of the user in the at least one third
image meets the adjusted selected criteria, obtaining one or more
fourth images of the face of the user using the camera located on
the device; and assessing the fourth images using the facial
recognition authentication process to determine if the user is the
authorized user of the device.
2. The method of claim 1, wherein assessing the at least one first
image to determine if the face of the user in the at least one
first image meets the one or more selected criteria comprises:
encoding the at least one first image to generate at least one
feature vector, wherein the feature vector represents one or more
facial features of the user in the at least one first image; and
assessing the at least one feature vector to determine if the face
of the user in the at least one first image meets the one or more
selected criteria.
3. The method of claim 1, wherein the selected criteria for the at
least one first image comprises one or more of the following
criteria: a minimum portion of the face of the user being in a
field of view of the camera, a pose of the face being proper, a
distance between the face of the user and the camera being in a
selected distance range, the face of the user having occlusion
below a minimum value, and eyes of the user not being closed.
4. The method of claim 1, wherein the selected criteria comprises a
distance between the face of the user and the camera being in a
selected distance range, and wherein adjusting the selected
criteria comprises reducing the selected distance range.
5. The method of claim 1, wherein the selected criteria comprises
the face of the user having occlusion below a minimum value, and
wherein adjusting the selected criteria comprises reducing the
minimum value of occlusion in the selected criteria.
6. The method of claim 1, further comprising, in response to
determining that the face of the user in the at least one third
image does not meet the adjusted selected criteria: obtaining and
assessing additional third images until at least one of the
additional third images meets the adjusted selected criteria.
7. The method of claim 1, further comprising automatically
adjusting the at least one of the selected criteria and obtaining
the at least one third image of the user using the camera without
input from the user through the user interface.
8. The method of claim 1, further comprising providing a
notification to the user on the user interface in response to the
user being determined to not be an authorized user of the device by
the facial recognition authentication process.
9. A device, comprising: a computer processor; a memory; a camera;
at least one illuminator providing infrared illumination; circuitry
coupled to the camera and the illuminator, wherein the circuitry is
configured to: obtain at least one first image of the user using
the in response to receiving a request from a user to unlock the
device; assess the at least one first image to determine if a face
of the user in the at least one first image meets one or more
selected criteria; obtain one or more second images of the face of
the user using the camera in response to determining that the face
of the user in the at least one first image meets the one or more
selected criteria; operate a facial recognition authentication
process on the second images to determine if the user is an
authorized user of the device, wherein during the facial
recognition authentication process, the circuitry is configured to:
encode the second images to generate at least one feature vector,
wherein the feature vector represents one or more facial features
of the user in the second images; compare the feature vector to one
or more reference templates stored in the memory of the device to
obtain a matching score; and authorize the user to perform at least
one operation on the device that requires authentication if the
matching score is above an unlock threshold; wherein, in response
to the user being determined to not be an authorized user of the
device by the facial recognition authentication process, the
circuitry is configured to: adjust at least one of the selected
criteria; obtain at least one third image of the user using the
camera; assess the at least one third image to determine if the
face of the user in the at least one third image meets the adjusted
selected criteria; obtain one or more fourth images of the face of
the user using the camera in response to determining that the face
of the user in the at least one third image meets the adjusted
selected criteria; and operate the facial recognition
authentication process on the fourth images to determine if the
user is the authorized user of the device.
10. The device of claim 9, wherein the at least one illuminator
comprises a flood infrared illuminator and a pattern infrared
illuminator.
11. The device of claim 9, further comprising a user interface on
the device, wherein the user interface is configured to receive the
request to unlock the device.
12. The device of claim 9, wherein a portion of the circuitry is
configured to provide a signal that the facial recognition
authentication process has failed, and wherein another portion of
the circuitry is configured to adjust at least one of the selected
criteria in response to receiving the signal.
13. The device of claim 9, wherein the circuitry is configured to
automatically adjust the at least one of the selected criteria and
automatically obtain the at least one third image of the user using
the camera without input from the user.
14. The device of claim 9, further comprising a display on the
device, wherein the circuitry is configured to provide a
notification to the user on the display in response to the user
being determined to not be an authorized user of the device by the
facial recognition authentication process.
15. A method, comprising: receiving, on a user interface associated
with a device comprising a computer processor and a memory, an
unlock request for the device from a user; in response to receiving
the unlock request, obtaining at least one first image of the user
using a camera located on the device; assessing the at least one
first image using a facial recognition authentication process to
determine if the user is an authorized user of the device, wherein
the facial recognition authentication process comprises: encoding
the first images to generate at least one feature vector, wherein
the feature vector represents one or more facial features of the
user in the first images; comparing the feature vector to one or
more reference templates stored in the memory of the device to
obtain a matching score; and authorizing the user to perform at
least one operation on the device that requires authentication if
the matching score is above an unlock threshold; in response to the
user being determined to not be an authorized user of the device by
the facial recognition authentication process: adjusting one or
more selected criteria for obtaining images on the device, wherein
the selected criteria comprises a distance between the face of the
user and the camera being in a selected distance range, and wherein
adjusting the selected criteria comprises reducing the selected
distance range; obtaining at least one second image of the user
using the camera located on the device; assessing the at least one
second image using the facial recognition authentication process to
determine if the user is the authorized user of the device; and
unlocking the device in response to determining that the user is
the authorized user.
16. The method of claim 15, wherein the selected criteria comprises
at least some portion of the face of the user being in the
image.
17. The method of claim 15, wherein the selected criteria comprises
the face of the user having occlusion below a minimum value, and
wherein adjusting the selected criteria comprises reducing the
minimum value of occlusion in the selected criteria.
Description
BACKGROUND
1. Technical Field
Embodiments described herein relate to methods and systems for face
detection and recognition in images captured by a camera on a
device. More particularly, embodiments described herein relate to
retrying of a facial recognition authentication process after the
facial recognition authentication process fails at an attempt to
authenticate a user.
2. Description of Related Art
Biometric authentication processes are being used more frequently
to allow users to more readily access their devices without the
need for passcode or password authentication. One example of a
biometric authentication process is fingerprint authentication
using a fingerprint sensor. Facial recognition is another biometric
process that may be used for authentication of an authorized user
of a device. Facial recognition processes are generally used to
identify individuals in an image and/or compare individuals in
images to a database of individuals to match the faces of
individuals.
For authentication using facial recognition, the facial recognition
system may sometimes encounter problems in authenticating an
authorized user when images captured during the authentication
process are captured under non-ideal conditions. For example, the
user's face being too far/too close to the camera, the user's face
having some occlusion in the captured image, and/or the user's
attention or pose in the images being less than ideal may prevent
matching (e.g., authentication) of the authorized user in the
captured images with the authorized user enrolled on the device. If
the user repeatedly fails to be authorized using the facial
recognition authentication process, the user may become frustrated
with the experience and look for other avenues of authentication
and/or search for another device to use instead of the current
device.
SUMMARY
In certain embodiments, in the event a facial recognition
authentication process fails to authenticate a user, the facial
recognition authentication process may re-initiate and retry to
authenticate the user using newly captured images. The
re-initiation of the facial recognition authentication process may
be automatic without input from the user (e.g., the user does not
have to provide additional input to re-initiate the process).
Automatic re-initiation of the facial recognition authentication
process may provide a more satisfying user experience (if the
re-attempted process is successful in authenticating the user).
In some embodiments, one or more criteria for the images are
adjusted when the facial recognition authentication process is
re-initiated. The criteria for the images may include criteria that
are useful in determining that the images can be successfully
operated on to authenticate the user. Examples of criteria for the
images include distance between the camera and the user's face,
attention of the user in the images, pose of the user's face in the
images (e.g., pitch, yaw, and roll of the face), and/or occlusion
of the user's face in the images. Adjusting one or more of the
criteria before re-initiating the facial recognition authentication
process may improve the chances of success in the subsequent facial
recognition authentication process.
BRIEF DESCRIPTION OF THE DRAWINGS
Features and advantages of the methods and apparatus of the
embodiments described in this disclosure will be more fully
appreciated by reference to the following detailed description of
presently preferred but nonetheless illustrative embodiments in
accordance with the embodiments described in this disclosure when
taken in conjunction with the accompanying drawings in which:
FIG. 1 depicts a representation of an embodiment of a device
including a camera.
FIG. 2 depicts a representation of an embodiment of a camera.
FIG. 3 depicts a representation of an embodiment of a processor on
a device.
FIG. 4 depicts a flowchart of an embodiment of an image enrollment
process for an authorized user of a device.
FIG. 5 depicts a representation of an embodiment of a feature space
with feature vectors after an enrollment process.
FIG. 6 depicts a representation of an embodiment of a template
space for an enrollment profile in a memory of a device.
FIG. 7 depicts a flowchart of an embodiment of a facial recognition
authentication process.
FIG. 8 depicts a block diagram of one embodiment of an exemplary
computer system.
FIG. 9 depicts a block diagram of one embodiment of a computer
accessible storage medium.
While embodiments described in this disclosure may be susceptible
to various modifications and alternative forms, specific
embodiments thereof are shown by way of example in the drawings and
will herein be described in detail. It should be understood,
however, that the drawings and detailed description thereto are not
intended to limit the embodiments to the particular form disclosed,
but on the contrary, the intention is to cover all modifications,
equivalents and alternatives falling within the spirit and scope of
the appended claims. The headings used herein are for
organizational purposes only and are not meant to be used to limit
the scope of the description. As used throughout this application,
the word "may" is used in a permissive sense (i.e., meaning having
the potential to), rather than the mandatory sense (i.e., meaning
must). Similarly, the words "include", "including", and "includes"
mean including, but not limited to.
Various units, circuits, or other components may be described as
"configured to" perform a task or tasks. In such contexts,
"configured to" is a broad recitation of structure generally
meaning "having circuitry that" performs the task or tasks during
operation. As such, the unit/circuit/component can be configured to
perform the task even when the unit/circuit/component is not
currently on. In general, the circuitry that forms the structure
corresponding to "configured to" may include hardware circuits
and/or memory storing program instructions executable to implement
the operation. The memory can include volatile memory such as
static or dynamic random access memory and/or nonvolatile memory
such as optical or magnetic disk storage, flash memory,
programmable read-only memories, etc. The hardware circuits may
include any combination of combinatorial logic circuitry, clocked
storage devices such as flops, registers, latches, etc., finite
state machines, memory such as static random access memory or
embedded dynamic random access memory, custom designed circuitry,
programmable logic arrays, etc. Similarly, various
units/circuits/components may be described as performing a task or
tasks, for convenience in the description. Such descriptions should
be interpreted as including the phrase "configured to." Reciting a
unit/circuit/component that is configured to perform one or more
tasks is expressly intended not to invoke 35 U.S.C. .sctn. 112(f)
interpretation for that unit/circuit/component.
In an embodiment, hardware circuits in accordance with this
disclosure may be implemented by coding the description of the
circuit in a hardware description language (HDL) such as Verilog or
VHDL. The HDL description may be synthesized against a library of
cells designed for a given integrated circuit fabrication
technology, and may be modified for timing, power, and other
reasons to result in a final design database that may be
transmitted to a foundry to generate masks and ultimately produce
the integrated circuit. Some hardware circuits or portions thereof
may also be custom-designed in a schematic editor and captured into
the integrated circuit design along with synthesized circuitry. The
integrated circuits may include transistors and may further include
other circuit elements (e.g. passive elements such as capacitors,
resistors, inductors, etc.) and interconnect between the
transistors and circuit elements. Some embodiments may implement
multiple integrated circuits coupled together to implement the
hardware circuits, and/or discrete elements may be used in some
embodiments.
The scope of the present disclosure includes any feature or
combination of features disclosed herein (either explicitly or
implicitly), or any generalization thereof, whether or not it
mitigates any or all of the problems addressed herein. Accordingly,
new claims may be formulated during prosecution of this application
(or an application claiming priority thereto) to any such
combination of features. In particular, with reference to the
appended claims, features from dependent claims may be combined
with those of the independent claims and features from respective
independent claims may be combined in any appropriate manner and
not merely in the specific combinations enumerated in the appended
claims.
DETAILED DESCRIPTION OF EMBODIMENTS
This specification includes references to "one embodiment" or "an
embodiment." The appearances of the phrases "in one embodiment" or
"in an embodiment" do not necessarily refer to the same embodiment,
although embodiments that include any combination of the features
are generally contemplated, unless expressly disclaimed herein.
Particular features, structures, or characteristics may be combined
in any suitable manner consistent with this disclosure.
As described herein, one aspect of the present technology is the
gathering and use of data available from various sources to improve
the operation and access to devices. The present disclosure
contemplates that in some instances, this gathered data may include
personal information data that uniquely identifies or can be used
to contact or locate a specific person. Such personal information
data can include image data (e.g., data from images of the user),
demographic data, location-based data, telephone numbers, email
addresses, home addresses, or any other identifying information.
For image data, the personal information data may only include data
from the images of the user and not the images themselves.
The present disclosure recognizes that the use of such personal
information data, in the present technology, can be used to the
benefit of users. For example, the personal information data can be
used to control unlocking and/or authorizing devices using facial
recognition. Accordingly, use of such personal information data
enables calculated control of access to devices. Further, other
uses for personal information data that benefit the user are also
contemplated by the present disclosure.
The present disclosure further contemplates that the entities
responsible for the collection, analysis, disclosure, transfer,
storage, or other use of such personal information data will comply
with well-established privacy policies and/or privacy practices. In
particular, such entities should implement and consistently use
privacy policies and practices that are generally recognized as
meeting or exceeding industry or governmental requirements for
maintaining personal information data private and secure. For
example, in the case of unlocking and/or authorizing devices using
facial recognition, personal information from users should be
collected for legitimate and reasonable uses of the entity, as such
uses pertain only to operation of the devices, and not shared or
sold outside of those legitimate uses. Further, such collection
should occur only after receiving the informed consent of the user
and the personal information data should remain secured on the
device on which the personal information is collected.
Additionally, such entities would take any needed steps for
safeguarding and securing access to such personal information data
and ensuring that others with access to the personal information
data adhere to their privacy policies and procedures. Further, such
entities can subject themselves to evaluation by third parties to
certify their adherence to widely accepted privacy policies and
practices.
Despite the foregoing, the present disclosure also contemplates
embodiments in which users selectively block the use of, or access
to, personal information data. That is, the present disclosure
contemplates that hardware and/or software elements can be provided
to prevent or block access to such personal information data. For
example, the present technology can be configured to allow users to
select to "opt in" or "opt out" of participation in the collection
of personal information data during registration for services.
FIG. 1 depicts a representation of an embodiment of a device
including a camera. In certain embodiments, device 100 includes
camera 102, processor 104, memory 106, and display 108. Device 100
may be a small computing device, which may be, in some cases, small
enough to be handheld (and hence also commonly known as a handheld
computer or simply a handheld). In certain embodiments, device 100
is any of various types of computer systems devices which are
mobile or portable and which perform wireless communications using
WLAN communication (e.g., a "mobile device"). Examples of mobile
devices include mobile telephones or smart phones, and tablet
computers. Various other types of devices may fall into this
category if they include wireless or RF communication capabilities
(e.g., Wi-Fi, cellular, and/or Bluetooth), such as laptop
computers, portable gaming devices, portable Internet devices, and
other handheld devices, as well as wearable devices such as smart
watches, smart glasses, headphones, pendants, earpieces, etc. In
general, the term "mobile device" can be broadly defined to
encompass any electronic, computing, and/or telecommunications
device (or combination of devices) which is easily transported by a
user and capable of wireless communication using, for example,
WLAN, Wi-Fi, cellular, and/or Bluetooth. In certain embodiments,
device 100 includes any device used by a user with processor 104,
memory 106, and display 108. Display 108 may be, for example, an
LCD screen or touchscreen. In some embodiments, display 108
includes a user input interface for device 100 (e.g., the display
allows interactive input for the user).
Camera 102 may be used to capture images of the external
environment of device 100. In certain embodiments, camera 102 is
positioned to capture images in front of display 108. Camera 102
may be positioned to capture images of the user (e.g., the user's
face) while the user interacts with display 108. FIG. 2 depicts a
representation of an embodiment of camera 102. In certain
embodiments, camera 102 includes one or more lenses and one or more
image sensors 103 for capturing digital images. Digital images
captured by camera 102 may include, for example, still images,
video images, and/or frame-by-frame images.
In certain embodiments, camera 102 includes image sensor 103. Image
sensor 103 may be, for example, an array of sensors. Sensors in the
sensor array may include, but not be limited to, charge coupled
device (CCD) and/or complementary metal oxide semiconductor (CMOS)
sensor elements to capture infrared images (IR) or other
non-visible electromagnetic radiation. In some embodiments, camera
102 includes more than one image sensor to capture multiple types
of images. For example, camera 102 may include both IR sensors and
RGB (red, green, and blue) sensors. In certain embodiments, camera
102 includes illuminators 105 for illuminating surfaces (or
subjects) with the different types of light detected by image
sensor 103. For example, camera 102 may include an illuminator for
visible light (e.g., a "flash illuminator), illuminators for RGB
light, and/or illuminators for infrared light (e.g., a flood IR
source and a pattern (speckle pattern) projector). In some
embodiments, the flood IR source and pattern projector are other
wavelengths of light (e.g., not infrared). In certain embodiments,
illuminators 105 include an array of light sources such as, but not
limited to, VCSELs (vertical-cavity surface-emitting lasers). In
some embodiments, image sensors 103 and illuminators 105 are
included in a single chip package. In some embodiments, image
sensors 103 and illuminators 105 are located on separate chip
packages.
In certain embodiments, image sensor 103 is an IR image sensor and
the image sensor is used to capture infrared images used for face
detection, facial recognition authentication, and/or depth
detection. Other embodiments of image sensor 103 (e.g., an RGB
image sensor) may also be contemplated for use in face detection,
facial recognition authentication, and/or depth detection as
described herein. For face detection, illuminator 105A may provide
flood IR illumination to flood the subject with IR illumination
(e.g., an IR flashlight) and image sensor 103 may capture images of
the flood IR illuminated subject. Flood IR illumination images may
be, for example, two-dimensional images of the subject illuminated
by IR light.
For depth detection or generating a depth map image, illuminator
105B may provide IR illumination with a pattern (e.g., patterned
infrared (IR) illumination). The pattern may be a pattern of light
with a known, and controllable, configuration and pattern projected
onto a subject (e.g., a structured pattern of light). In certain
embodiments, the pattern is a speckle pattern (e.g., a pattern of
dots). The pattern may, however, include any structured or
semi-structured pattern of light features. For example, the pattern
may include, but not be limited to, dots, speckles, stripes,
dashes, nodes, edges, and combinations thereof.
Illuminator 105B may include a VCSEL array configured to form the
pattern or a light source and patterned transparency configured to
form the pattern. The configuration and pattern of the pattern
provided by illuminator 105B may be selected, for example, based on
a desired pattern density (e.g., speckle or dot density) at the
subject. Image sensor 103 may capture images of the subject
illuminated by the pattern. The captured image of the pattern on
the subject may be assessed (e.g., analyzed and/or processed) by an
imaging and processing system (e.g., an image signal processor
(ISP) as described herein) to produce or estimate a
three-dimensional map of the subject (e.g., a depth map or depth
map image of the subject). Examples of depth map imaging are
described in U.S. Pat. No. 8,150,142 to Freedman et al., U.S. Pat.
No. 8,749,796 to Pesach et al., and U.S. Pat. No. 8,384,997 to
Shpunt et al., which are incorporated by reference as if fully set
forth herein, and in U.S. Patent Application Publication No.
2016/0178915 to Mor et al., which is incorporated by reference as
if fully set forth herein.
In certain embodiments, images captured by camera 102 include
images with the user's face (e.g., the user's face is included in
the images). An image with the user's face may include any digital
image with at least some portion of the user's face shown within
the frame of the image. Such an image may include just the user's
face or may include the user's face in a smaller part or portion of
the image. The user's face may be captured with sufficient
resolution in the image to allow image processing of one or more
features of the user's face in the image.
Images captured by camera 102 may be processed by processor 104.
FIG. 3 depicts a representation of an embodiment of processor 104
included in device 100. Processor 104 may include circuitry
configured to execute instructions defined in an instruction set
architecture implemented by the processor. Processor 104 may
execute the main control software of device 100, such as an
operating system. Generally, software executed by processor 104
during use may control the other components of device 100 to
realize the desired functionality of the device. The processors may
also execute other software. These applications may provide user
functionality, and may rely on the operating system for lower-level
device control, scheduling, memory management, etc.
In certain embodiments, processor 104 includes image signal
processor (ISP) 110. ISP 110 may include circuitry suitable for
processing images (e.g., image signal processing circuitry)
received from camera 102. ISP 110 may include any hardware and/or
software (e.g., program instructions) capable of processing or
analyzing images captured by camera 102.
In certain embodiments, processor 104 includes secure enclave
processor (SEP) 112. In some embodiments, SEP 112 is involved in a
facial recognition authentication process involving images captured
by camera 102 and processed by ISP 110. SEP 112 may be a secure
circuit configured to authenticate an active user (e.g., the user
that is currently using device 100) as authorized to use device
100. A "secure circuit" may be a circuit that protects an isolated,
internal resource from being directly accessed by an external
circuit. The internal resource may be memory (e.g., memory 106)
that stores sensitive data such as personal information (e.g.,
biometric information, credit card information, etc.), encryptions
keys, random number generator seeds, etc. The internal resource may
also be circuitry that performs services/operations associated with
sensitive data. As described herein, SEP 112 may include any
hardware and/or software (e.g., program instructions) capable of
authenticating a user using the facial recognition authentication
process. The facial recognition authentication process may
authenticate a user by capturing images of the user with camera 102
and comparing the captured images to previously collected images of
an authorized user for device 100. In some embodiments, the
functions of ISP 110 and SEP 112 may be performed by a single
processor (e.g., either ISP 110 or SEP 112 may perform both
functionalities and the other processor may be omitted).
In certain embodiments, processor 104 performs an enrollment
process (e.g., image enrollment process 200, as shown in FIG. 4, or
a registration process) to capture images (e.g., the previously
collected images) for an authorized user of device 100. During the
enrollment process, camera module 102 may capture (e.g., collect)
images and/or image data from an authorized user in order to permit
SEP 112 (or another security process) to subsequently authenticate
the user using the facial recognition authentication process. In
some embodiments, the images and/or image data (e.g., feature
vector data from the images) from the enrollment process are used
to generate templates in device 100. The templates may be stored,
for example, in a template space in memory 106 of device 100. In
some embodiments, the template space may be updated by the addition
and/or subtraction of templates from the template space. A template
update process (e.g., first template update process 300 and/or
second template update process 400 described herein) may be
performed by processor 104 to add and/or subtract templates from
the template space. For example, the template space may be updated
with additional templates to adapt to changes in the authorized
user's appearance and/or changes in hardware performance over time.
Templates may be subtracted from the template space to compensate
for the addition of templates when the template space for storing
templates is full.
In some embodiments, camera module 102 captures multiple pairs of
images for a facial recognition session. Each pair may include an
image captured using a two-dimensional capture mode (e.g., a flood
IR image) and an image captured using a three-dimensional capture
mode (e.g., a patterned illumination image used to generate a depth
map image). In certain embodiments, ISP 110 and/or SEP 112 process
the flood IR images and patterned illumination images independently
of each other before a final authentication decision is made for
the user. For example, ISP 110 may process the images independently
to determine characteristics of each image separately. SEP 112 may
then compare the separate image characteristics with stored
templates for each type of image to generate an authentication
score (e.g., a matching score or other ranking of matching between
the user in the captured image and in the stored templates) for
each separate image. The authentication scores for the separate
images (e.g., the flood IR and patterned illumination images) may
be combined to make a decision on the identity of the user and, if
authenticated, allow the user to use device 100 (e.g., unlock the
device).
In some embodiments, ISP 110 and/or SEP 112 combine the images in
each pair to provide a composite image that is used for facial
recognition. In some embodiments, ISP 110 processes the composite
image to determine characteristics of the image, which SEP 112 may
compare with the stored templates to make a decision on the
identity of the user and, if authenticated, allow the user to use
device 100.
In some embodiments, the combination of flood IR image data and
patterned illumination image data may allow for SEP 112 to compare
faces in a three-dimensional space. In some embodiments, camera
module 102 communicates image data to SEP 112 via a secure channel.
The secure channel may be, for example, either a dedicated path for
communicating data (i.e., a path shared by only the intended
participants) or a dedicated path for communicating encrypted data
using cryptographic keys known only to the intended participants.
In some embodiments, camera module 102 and/or ISP 110 may perform
various processing operations on image data before supplying the
image data to SEP 112 in order to facilitate the comparison
performed by the SEP.
In certain embodiments, processor 104 operates one or more machine
learning models. Machine learning models may be operated using any
combination of hardware and/or software (e.g., program
instructions) located in processor 104 and/or on device 100. In
some embodiments, one or more neural network modules 114 are used
to operate the machine learning models on device 100. Neural
network modules 114 may be located in ISP 110 and/or SEP 112.
Neural network module 114 may include any combination of hardware
and/or software (e.g., program instructions) located in processor
104 and/or on device 100. In some embodiments, neural network
module 114 is a multi-scale neural network or another neural
network where the scale of kernels used in the network can vary. In
some embodiments, neural network module 114 is a recurrent neural
network (RNN) such as, but not limited to, a gated recurrent unit
(GRU) recurrent neural network or a long short-term memory (LSTM)
recurrent neural network.
Neural network module 114 may include neural network circuitry
installed or configured with operating parameters that have been
learned by the neural network module or a similar neural network
module (e.g., a neural network module operating on a different
processor or device). For example, a neural network module may be
trained using training images (e.g., reference images) and/or other
training data to generate operating parameters for the neural
network circuitry. The operating parameters generated from the
training may then be provided to neural network module 114
installed on device 100. Providing the operating parameters
generated from training to neural network module 114 on device 100
allows the neural network module to operate using training
information programmed into the neural network module (e.g., the
training-generated operating parameters may be used by the neural
network module to operate on and assess images captured by the
device).
FIG. 4 depicts a flowchart of an embodiment of image enrollment
process 200 for an authorized user of device 100. Process 200 may
be used to create an enrollment profile for an authorized user of
device 100 that is stored in the device (e.g., in a memory coupled
to SEP 112). The enrollment profile may include one or more
templates for the authorized user created using process 200. The
enrollment profile and the templates associated with the enrollment
profile may be used in a facial recognition process to allow (e.g.,
authorize) the user to use the device and/or perform operations on
the device (e.g., unlock the device).
In certain embodiments, process 200 is used when device 100 is used
a first time by the authorized user and/or when the user opts to
create an enrollment profile for a facial recognition process. For
example, process 200 may be initiated when device 100 is first
obtained by the authorized user (e.g., purchased by the authorized
user) and turned on for the first time by the authorized user. In
some embodiments, process 200 may be initiated by the authorized
user when the user desires to enroll in a facial recognition
process, update security settings for device 100, re-enroll, and/or
add an enrollment profile on the device.
In certain embodiments, process 200 begins with authenticating the
user in 202. In 202, the user may be authenticated on device 100
using a non-facial authentication process. For example, the user
may be authenticated as an authorized user by entering a passcode,
entering a password, or using another user authentication protocol
other than facial recognition. After the user is authenticated in
202, one or more enrollment (e.g., reference or registration)
images of the user are captured in 204. The enrollment images may
include images of the user illuminated by flood illuminator 105A
(e.g., flood IR images) and/or images of the user illuminated by
illuminator 105B (e.g., patterned illumination images). As
described herein, flood IR images and patterned illumination images
may be used independently and/or in combination in facial
recognition processes on device 100 (e.g. the images may
independently be used to provide an authentication decision and the
decisions may be combined to determine a final decision on user
authentication).
The enrollment images may be captured using camera 102 as the user
interacts with device 100. For example, the enrollment images may
be captured as the user follows prompts on display 108 of device
100. The prompts may include instructions for the user to make
different motions and/or poses while the enrollment images are
being captured. During 204, camera 102 may capture multiple images
for each motion and/or pose performed by the user. Capturing images
for different motions and/or different poses of the user where the
images still have a relatively clear depiction of the user may be
useful in providing a better variety of enrollment images that
enable the user to be authenticated without having to be in a
limited or restricted position relative to camera 102 on device
100.
After the multiple enrollment images are captured in 204, selection
of enrollment images for further image processing may be made in
206. Selection of enrollment images 206, and further processing of
the images, may be performed by ISP 110 and/or SEP 112. Selection
of enrollment images for further processing may include selecting
images that are suitable for generating templates. For example, the
selection of images that are suitable for use generating templates
in 206 may include assessing one or more selected criteria for the
images and selecting images that meet the selected criteria. The
selected images may be used to generate templates for the user.
Selected criteria may include, but not be limited to, the face of
the user being in the field of view of the camera, a pose of the
user's face being proper (e.g., the user's face is not turned too
far in any direction from the camera (i.e., the pitch, yaw, and/or
roll of the face are not above certain levels)), a distance between
camera 102 and the face of the user being in a selected distance
range, the face of the user having occlusion below a minimum value
(e.g., the user's face is not occluded (blocked) more than a
minimum amount by another object), the user paying attention to the
camera (e.g., eyes of the user looking at the camera), eyes of the
user not being closed, and proper lighting (illumination) in the
image. In some embodiments, if more than one face is detected in an
enrollment image, the enrollment image is rejected and not used
(e.g., not selected) for further processing. Selection of images
suitable for further processing may be rule based on the images
meeting a certain number of the selected criteria or all of the
selected criteria. In some embodiments, occlusion maps and/or
landmark feature maps are used in identifying features of the user
(e.g., facial features such as eyes, nose, and mouth) in the images
and assessing the selected criteria in the images.
After images are selected in 206, features of the user in the
selected (template) images may be encoded in 208. Encoding of the
selected images may include encoding features (e.g., facial
features) of the user to define the features in the images as one
or more feature vectors in a feature space. Feature vectors 210 may
be the output of the encoding in 208. A feature space may be an
n-dimensional feature space. A feature vector may be an
n-dimensional vector of numerical values that define features from
the image in the feature space (e.g., the feature vector may be a
vector of numerical values that define facial features of the user
in the image).
FIG. 5 depicts a representation of an embodiment of feature space
212 with feature vectors 210. Each feature vector 210 (black dot)
may define facial features for the user from either a single image,
from a composite image (e.g., an image that is a composite of
several images), or from multiple images. As feature vectors 210
are generated from a single user's facial features, the feature
vectors may be similar to one another because the feature vectors
are associated with the same person and may have some "clustering",
as shown by circle 211 in FIG. 5. Feature vectors 256A and 256B
(open diamonds) are feature vectors obtained from facial
recognition process 250, described below.
As shown in FIG. 4, process 200 may include, in 214, storing
feature vectors 210 in a memory of device 100 (e.g., a memory
protected by SEP 112). In certain embodiments, feature vectors 210
are stored as static templates 216 (e.g., enrollment templates or
reference templates) in a template space of the memory (e.g.,
template space 220 described below). Static templates 216 may be
used for the enrollment profile created by process 200. In some
embodiments, static templates 216 (and other templates described
herein) include separate templates for feature vectors obtained
from the enrollment flood IR images and for feature vectors
obtained from the enrollment patterned illumination images. It is
to be understood that the separate templates obtained from flood IR
images and patterned illumination images (e.g., images used to
generate depth map images) may be used independently and/or in
combination during additional processes described herein. For
simplicity in this disclosure, static templates 216 are described
generically and it should be understood that static templates 216
(and the use of the templates) may refer to either templates
obtained from flood IR images or templates obtained from patterned
illumination images. In some embodiments, a combination of the
flood IR images and patterned illumination images may be used to
generate templates. For example, pairs of feature vectors obtained
from flood IR images and patterned illumination images may be
stored in static templates 216 to be used in one or more facial
recognition processes on device 100.
FIG. 6 depicts a representation of an embodiment of template space
220 for an enrollment profile in memory 106 of device 100. In
certain embodiments, template space 220 is located in a portion of
memory 106 of device 100 protected by SEP 112. In some embodiments,
template space 220 includes static portion 222 and dynamic portion
224. Static templates 216 may be, for example, added to static
portion 222 of template space 220 (e.g., the templates are
permanently added to the memory and are not deleted or changed
unless the device is reset). In some embodiments, static portion
222 includes a certain number of static templates 216. For example,
for the embodiment of template space 220 depicted in FIG. 6, six
static templates 216 are allowed in static portion 222. In some
embodiments, nine static templates 216 may be allowed in static
portion 222. Other numbers of static templates 216 in static
portion 222 may also be contemplated. After the enrollment process
for the enrollment profile is completed and static templates 216
are added to static portion 222, additional dynamic templates 226
may be added to dynamic portion 224 of template space 220 for the
enrollment profile (e.g., a portion from which templates may be
added and deleted without a device reset being needed).
Static templates 216 may thus be enrollment templates (or reference
templates) generated by enrollment process 200 for the enrollment
profile associated with the enrollment process. After enrollment
process 200 is completed, a selected number of static templates 216
are stored in static portion 222 of template space 220 for the
enrollment profile. The number of static templates 216 stored in
static portion 222 after enrollment process 200 may vary depending
on, for example, the number of different feature vectors obtained
during the enrollment process, which may be based on the number of
images selected to be suitable for use as template images, or a
desired number of templates for the device. After enrollment
process 200, static templates 216 include feature vectors 210
(e.g., the enrollment or reference feature vectors) that can be
used for facial recognition of the authorized user associated with
the enrollment profile. Thus, template space 220 may be used in a
facial recognition authentication process to authorize the user
associated with the enrollment profile.
FIG. 7 depicts a flowchart of an embodiment of facial recognition
authentication process 250. Process 250 may be used to authenticate
a user as an authorized user of device 100 using facial recognition
of the user. In certain embodiments, process 250 is used to
authenticate a user using an enrollment profile (e.g., template
space 220) on device 100. Authentication of the authorized user may
allow the user to access and use device 100 (e.g., unlock the
device) and/or have access to a selected functionality of the
device (e.g., unlocking a function of an application running on the
device, payment systems (i.e., making a payment), access to
personal data, expanded view of notifications, etc.). In certain
embodiments, process 250 is used as a primary biometric
authentication process for device 100 (after enrollment of the
authorized user). In some embodiments, process 250 is used as an
authentication process in addition to another authentication
process (e.g., fingerprint authentication, another biometric
authentication, passcode entry, password entry, and/or pattern
entry). In some embodiments, another authentication process (e.g.,
passcode entry, pattern entry, other biometric authentication) may
be used to access device 100 if the user fails to be authenticated
using process 250.
In certain embodiments, process 250 begins with sub-process 300.
Sub-process 300 may be an initial process (e.g., a "gate process)
to assess an initial image captured by device 100 to determine if
process 250 should continue with further downstream processing to
authenticate the user. Sub-process 300 may begin with capturing an
image of the user attempting to be authenticated for access to
device 100 in 302 (e.g., the camera captures a "gate" image of the
user). In certain embodiments, in 302, camera 102 captures a flood
IR image of the user for the gate image. It is to be understood
that the gate image may be a single image of the face of the user
(e.g., a single flood IR image) or the gate image may be a series
of several images of the face of the user taken over a short period
of time (e.g., one second or less). In some embodiments, the gate
image may include a combination of flood IR images and patterned
illumination images (e.g., pairs of consecutive flood IR and
patterned illumination images). In some embodiments, the gate image
may be a composite of several images of the user illuminated by the
flood illuminator and/or the pattern illuminator.
Camera 102 may capture the gate image in response to a prompt by
the user. For example, the gate image may be captured when the user
attempts to access device 100 by pressing a button (e.g., a home
button or virtual button) on device 100, by moving the device into
a selected position relative to the user's face (e.g., the user
moves the device such that the camera is pointed at the user's face
or lifting the device from a table), and/or by making a specific
gesture or movement with respect to the device (e.g., (e.g.,
tapping on the screen, swiping the user's finger across the
display, or picking the device off the table).
In 304, the gate image may be encoded to define the facial features
of the user as one or more feature vectors in the feature space. In
some embodiments, one feature vector is defined for the gate image.
In some embodiments, multiple feature vectors are defined for the
gate image. Gate feature vector(s) 306 may be the output of the
encoding of the gate image in 304.
In certain embodiments, feature vectors 306 are assessed in 308 to
determine if selected criteria (e.g., "gate criteria") are met in
the images before the feature vectors are further processed to
unlock device 100 (e.g., before any attempt to match the feature
vectors with templates). Selected criteria that are assessed from
feature vectors 306 may include, but not be limited to, a minimum
portion of the face of the user being in the field of view of the
camera, a pose of the user's face being proper (e.g., the user's
face is not turned too far in any direction from the camera (i.e.,
the pitch, yaw, and/or roll of the face are not above certain
levels)), a distance between camera 102 and the face of the user
being within a selected distance range, the face of the user having
occlusion below a minimum value (e.g., the user's face is not
occluded (blocked) more than a minimum amount by another object),
the user paying attention to the camera (e.g., eyes of the user
looking at the camera), eyes of the user not being closed, proper
lighting (illumination or exposure) in the image, and/or the camera
being blocked or occluded (e.g., by a finger over the camera). The
types and number of selected criteria to be met before further
processing may be determined based on desired settings for process
250 and sub-process 300. For example, in some embodiments, the
selected criteria to be met may be chosen to provide qualities in
the images that are most likely to provide more accuracy in
downstream processing of the feature vectors (e.g., downstream
processing of the images).
If, at any time during operation of sub-process 300, all of the
selected criteria are met in 308, then process 250 continues with
capturing unlock attempt image(s) in 252. In some embodiments,
process 250 may continue in 252 if not all of the selected criteria
are met in 308. For example, process 250 may be allowed to continue
if 3 out of 4 selected criteria are met and/or if certain
combinations of selected criteria are met.
In certain embodiments, if any of the selected criteria are not met
in 308, then no further downstream processing in process 250 may
occur (e.g., no unlock attempt images are captured in 252). In some
embodiments, if a rejection of further processing based on the
selected criteria occurs in 308, then a new gate image is captured
in 302 and sub-process 300 processes the new image to see if the
new image meets the selected criteria. In some embodiments,
sub-process 300 may be repeated until the selected criteria are met
by an image captured in 302. In some embodiments, if sub-process
300 continues to reject gate images based on the selected criteria,
the sub-process (and process 250) are stopped and device 100 is
locked from further attempts to unlock the device. For example,
sub-process 300 may be repeated until a maximum number of gate
images are processed and/or a maximum time limit on attempting to
meet the selected criteria with a gate image is reached. In some
embodiments, sub-process 300 may continue to be repeated until the
display on device 100 turns off (e.g., the sub-process is
repeatedly tried as long as the display is on).
In some embodiments, after device 100 is locked by sub-process 300,
an error message may be displayed (e.g., on display 108) indicating
that facial recognition authentication process 250 has failed
and/or the desired operation of device 100 is restricted or
prevented from being performed. Device 100 may be locked from
further attempts to use facial authentication for a specified
period of time and/or until another authentication protocol is used
to unlock the device. For example, a passcode, a password, pattern
entry, a different form of biometric authentication, or another
authentication protocol may be used to unlock device 100.
In some embodiments, sub-process 300 operates without feedback to
the user (e.g., output or notification to the user) about rejection
of images and repeated attempts to capture an image that meets the
selected criteria. In some embodiments, the user is provided
feedback when a gate image is rejected. For example, an audio
message, a visual message, or another notification may be provided
if device 100 is too close to the user's face (e.g., the distance
between camera 102 and the user's face is shorter than the selected
distance range), is too far from the user's face (e.g., the
distance between the camera and the user's face exceeds the
selected distance range) and/or the user's face is occluded in the
view of the camera.
As described above, process 250 continues in 252 from sub-process
300 if all (or a selected number) of the selected criteria are met
in 308. In 252, camera 102 captures an additional image of the face
of the user to attempt to authenticate the user to access device
100 (e.g., the camera captures an "unlock attempt" image of the
user). It is to be understood that the unlock attempt image may be
a single image of the face of the user (e.g., a single flood IR
image or single patterned illumination image) or the unlock attempt
image may be a series of several images of the face of the user
taken over a short period of time (e.g., one second or less). In
some embodiments, the series of several images of the face of the
user includes pairs of flood IR images and patterned illumination
images (e.g., pairs of consecutive flood IR and patterned
illumination images). In some implementations, the unlock attempt
image may be a composite of several images of the user illuminated
by the flood illuminator and the pattern illuminator.
It is to be further understood that, as described herein, unlock
attempt images may include either flood IR images or patterned
illumination images (e.g., images used to generate depth map
images), or a combination thereof. Further, the unlock attempt
images may be processed in association with their corresponding
template (e.g., flood IR images with a template for flood IR
enrollment images) independently or in combination as needed.
In 254, the unlock attempt image is encoded to define the facial
features of the user as one or more feature vectors in the feature
space. In some embodiments, one feature vector is defined for the
unlock attempt image. In some embodiments, multiple feature vectors
are defined for the unlock attempt image. Unlock feature vector(s)
256 may be the output of the encoding of the unlock attempt image
in 254.
In certain embodiments, in 258, feature vector(s) 256 are compared
to feature vectors in the templates of template space 220 to get
matching score 260 for the unlock attempt image. In certain
embodiments, template space 220 is the template space for an
enrollment profile on device 100. Matching score 260 may be a score
of the differences between feature vector(s) 256 and feature
vectors in template space 220 (e.g., feature vectors in static
templates 216 and/or other dynamic templates 226 added to the
template space as described herein). The closer (e.g., the less
distance or less differences) that feature vector(s) 256 and the
feature vectors in template space 220 are, the higher matching
score 260 may be. For example, as shown in FIG. 5, feature vector
256A (open diamond) is closer to feature vectors 210 than feature
vector 256B (open diamond)(e.g., feature vector 256B is a further
outlier than feature vector 256A). Thus, feature vector 256A would
have a higher matching score than feature vector 256B. As feature
vector 256B is further away from feature vectors 210 than feature
vector 256A, the lower matching score for feature vector 256B means
less confidence that the face in the unlock attempt image
associated with feature vector 256B is the face of the authorized
user associated with the enrollment profile and template space
220.
In some embodiments, comparing feature vector(s) 256 and templates
from template space 220 to get matching score 260 includes using
one or more classifiers or a classification-enabled network to
classify and evaluate the differences between feature vector(s) 256
and templates from template space 220. Examples of different
classifiers that may be used include, but are not limited to,
linear, piecewise linear, nonlinear classifiers, support vector
machines, and neural network classifiers. In some embodiments,
matching score 260 is assessed using distance scores between
feature vector(s) 256 and templates from template space 220.
In 262, matching score 260 is compared to unlock threshold 264 for
device 100. Unlock threshold 264 may represent a minimum difference
(e.g., distance in the feature space) in features (as defined by
feature vectors) between the face of the authorized user and the
face of the user in the unlock attempt image that device 100
requires in order to unlock the device (or unlock a feature on the
device). For example, unlock threshold 264 may be a threshold value
that determines whether the unlock feature vectors (e.g., feature
vectors 256) are similar enough (e.g., close enough) to the
templates associated with the authorized user's face (e.g., static
templates 216 in template space 220). As further example, unlock
threshold 264 may be represented by circle 265 in feature space
212, depicted in FIG. 5. As shown in FIG. 5, feature vector 256A is
inside circle 265 and thus feature vector 256A would have matching
score 260 above unlock threshold 264. Feature vector 256B, however,
is outside circle 265 and thus feature vector 256B would have
matching score 260 below unlock threshold 264. In certain
embodiments, unlock threshold 264 is set during manufacturing
and/or by the firmware of device 100. In some embodiments, unlock
threshold 264 is updated (e.g., adjusted) by device 100 during
operation of the device as described herein.
As shown in FIG. 7, in 262, if matching score 260 is above unlock
threshold 264 (i.e., the user's face in the unlock attempt image
substantially matches the face of the authorized user), the user in
the unlock attempt image is authenticated as the authorized user
for the enrollment profile on device 100 and the device is unlocked
in 266. In 262, if matching score 260 is below unlock threshold 264
(e.g., not equal to or above the unlock threshold), then device 100
is not unlocked in 268 (e.g., the device remains locked). It should
be noted that device 100 may be either locked or unlocked if
matching score 260 is equal to unlock threshold 264 depending on a
desired setting for the unlock threshold (e.g., tighter or looser
restrictions). Additionally, either option for an equal matching
score comparison may be also applied as desired for other
embodiments described herein.
In certain embodiments, the unlock attempts are compared to a
threshold in 270. The threshold may be, for example, a maximum
number of unlock attempts allowed or a maximum allotted time for
unlock attempts. In certain embodiments, a number of unlock
attempts is counted (e.g., the number of attempts to unlock device
100 with a different unlock attempt image captured in 252) and
compared to the maximum number of unlock attempts allowed.
In certain embodiments, if the unlock attempts reaches the
threshold (e.g., number of unlock attempts reaches the maximum
number of attempts allowed), then device 100 is locked from further
attempts to use facial authentication in 272. In some embodiments,
when the device is locked in 272, an error message may be displayed
(e.g., on display 108) indicating that facial recognition
authentication process 250 has failed and/or the desired operation
of device 100 is restricted or prevented from being performed.
Device 100 may be locked from further attempts to use facial
authentication in 272 for a specified period of time and/or until
another authentication protocol is used to unlock the device. For
example, unlock options 274 may be used to unlock device 100.
Unlock options 274 may include the user being presented with one or
more options for proceeding with a different type of authentication
to unlock or access features on device 100 (e.g., the user is
presented options for proceeding with a second authentication
protocol). Presenting the options may include, for example,
displaying one or more options on display 108 of device 100 and
prompting the user through audible and/or visual communication to
select one of the displayed options to proceed with unlocking the
device or accessing features on the device. The user may then
proceed with unlocking/accessing device 100 using the selected
option and following additional audible and/or visual prompts as
needed. After successfully being authenticated using the selected
option, the user's initial request for unlocking/accessing device
100 may be granted. Unlock options 274 may include, but not be
limited to, using a passcode, a password, pattern entry, a
different form of biometric authentication, or another
authentication protocol to unlock device 100. In some embodiments,
unlock options 274 includes providing a "use
passcode/password/pattern" affordance that, when selected causes
display of a passcode/password/pattern entry user interface, or a
passcode/password/pattern entry user interface, or a "use
fingerprint" prompt that, when displayed, prompts the user to place
a finger on a fingerprint sensor for the device.
If the unlock attempts are below the threshold in 270 (e.g., number
of unlock attempts are below the maximum number of attempts
allowed), then process 250 may be run again (re-initiated)
beginning with another gate image in sub-process 300 (e.g.,
sub-process 300 is re-initiated and a new image of the user is
captured (such as a new flood IR image) in 302). In some
implementations, device 100 automatically captures the new gate
image of the user's face without prompting the user (e.g.,
capturing of the new image is automatically implemented and/or
hidden from the user). In some implementations, device 100 notifies
the user (either visually and/or audibly) that process 250 is being
re-initiated. In some embodiments, device 100 may prompt the user
to provide input to re-initiate process 250. For example, the user
may be prompted to acknowledge or otherwise confirm (either
visually and/or audibly) the attempt to re-initiate process
250.
In certain embodiments, as shown in FIG. 7, one or more of the
selected criteria (assessed in 308) are adjusted in 310 before
sub-process 300 is re-initiated. Adjusting the selected criteria in
310 may include providing tighter (e.g., more restrictive)
tolerances on one or more selected criteria. In some embodiments,
adjusting the selected criteria in 310 includes adjusting a
selected number of the selected criteria (e.g., only a subset of
the selected criteria are adjusted). The selected criteria chosen
to be adjusted in 310 may include selected criteria that, when
adjusted, are likely to provide more accuracy in the matching
process for unlock attempt images in process 250. For example,
adjusting of these selected criteria may increase the likelihood of
unlock attempt images captured in 252 being used to accurately
authorize the user in the re-initiation of process 250 (if the user
attempting to unlock device 100 is the authorized user and not an
unauthorized user).
In some embodiments, one of the selected criteria adjusted in 310
includes the selected distance range for the distance between
camera 102 and the user's face. In certain embodiments, the
selected distance range is reduced to a smaller range. For example,
the selected distance range may be reduced by about 5 cm, by about
10 cm, or by about 15 cm on one or both ends of the range. Thus,
gate images captured in successive re-initiation attempts of
sub-process 300 may have a reduced selected distance range for the
face of the user to meet when feature vectors 306 are assessed in
308. Reducing the selected distance range for the selected criteria
of distance between camera 102 and the user's face may increase the
likelihood that the user's face is in at an optimum distance from
the camera and more accurate feature vectors (e.g., feature vectors
likely to match the feature vectors in template 220) are extracted
(e.g., encoded) from images captured in 252.
In some embodiments, data for estimated distance between camera 102
and the user's face obtained from unlock attempt images captured in
252 are used in adjusting the selected distance range in 310.
Estimated data for distance between camera 102 and the user's face
may, for example, be obtained from patterned illumination images
(e.g., images used to generate depth map images) captured as unlock
attempt images (e.g., feature vectors 256 include distance data).
Depth map images generated from patterned illumination images may
be more accurate in estimating distance as the depth map images
include three-dimensional data. Thus, using distance data from the
depth map images may be useful in providing the adjustment needed
for the selected distance range in 310. For example, the distance
data from the depth map images may be used to assess if the user's
face was too near the lower limit or the upper limit of the
selected distance range in the previous unlock attempt images. The
depth map image distance data may then be used to adjust raise the
lower limit and/or lower the upper limit to increase the likelihood
of a better match in the re-initiation of process 250.
In some embodiments, one of the selected criteria adjusted in 310
the minimum value of occlusion in the gate image(s) captured in
302. For example, the minimum value of occlusion may be reduced
(e.g., less occlusion may be allowed in newly captured gate
images). In some embodiments, the minimum value of occlusion may be
reduced to essentially zero (no occlusion allowed) for the selected
criteria. Reducing the minimum value of occlusion may increase the
number of features in the user's face that are captured in the
unlock attempt images and the number of corresponding feature
vectors extracted. The increased number of feature vectors may
increase the accuracy and/or likelihood of matching the extracted
feature vectors (e.g., feature vectors 256) to the feature vectors
in template 220 during the re-initiation of process 250.
In some embodiments, other selected criteria are adjusted in 310.
For example, for the portion of the face of the user being in the
field of view of the camera, the minimum portion of the face of the
user needed in the field of view of the camera required may be
increased. For the pose of the user's face, the allowable levels
for the pitch, yaw, and/or roll of the face may be decreased (e.g.,
less pitch, yaw, and/or roll off a normal position are allowed).
For attention of the user, the limit on attention may be increased
(e.g., the tolerance for minimum attention may be increased). For
exposure, the range of exposure allowed in the image may be
decreased.
In some embodiments, the user is provided feedback when process 250
fails in 262 because the unlock attempt images are not matching
template 220 and the re-initiation of process 250 is being
implemented. The feedback may include providing an audio message, a
visual message, or another notification to the user on device 100.
In some embodiments, if the distance between the user's face and
camera 102 estimated from the depth map images indicates that the
user's face was near the lower limit of the selected distance
range, feedback may be provided that the user's face may be too
close to the camera and the user may move their face further away
from the camera for increase chances of authentication. The user
may also receive feedback if some occlusion is detected. Such
feedback may include asking the user to make sure the face and/or
camera are free from occlusions or obstructions. Additional
feedback may be provided to the user if a partial face of the user
is detected in the frame or scene of an image (e.g., when portions
of forehead, chin, left side, or right side of face are cut off).
The feedback may include asking the user to better position the
user's face in the frame or scene for capturing an image. For
example, the user may be asked to move his/her head towards a
center of the frame.
Adjusting one or more selected criteria in 310 for re-initiations
of process 250 may provide a more satisfying user experience for
the user. The experience may be more satisfying as the re-initiated
attempts (e.g., retry attempts) of process 250 to authenticate the
user may have a higher chance of success (if the user attempting to
unlock device 100 is the authorized user) with the tighter
tolerances placed on the selected criteria. Providing higher
chances of success for process 250 may decrease the frequency that
the user has to use a secondary authentication process to unlock
device 100. The user may also experience faster unlocking of device
100 with automated retry attempts of process 250. These factors may
increase the satisfaction of the authorized user in the facial
recognition authentication process, thus increasing the usability
of the facial recognition authentication process for the user.
In certain embodiments, one or more process steps described herein
may be performed by one or more processors (e.g., a computer
processor) executing instructions stored on a non-transitory
computer-readable medium. For example, process 200 and process 250,
shown in FIGS. 4 and 7, may have one or more steps performed by one
or more processors executing instructions stored as program
instructions in a computer readable storage medium (e.g., a
non-transitory computer readable storage medium).
FIG. 8 depicts a block diagram of one embodiment of exemplary
computer system 510. Exemplary computer system 510 may be used to
implement one or more embodiments described herein. In some
embodiments, computer system 510 is operable by a user to implement
one or more embodiments described herein such as process 200 and
process 250, shown in FIGS. 4 and 7. In the embodiment of FIG. 8,
computer system 510 includes processor 512, memory 514, and various
peripheral devices 516. Processor 512 is coupled to memory 514 and
peripheral devices 516. Processor 512 is configured to execute
instructions, including the instructions for process 200 and/or
process 250, which may be in software. In various embodiments,
processor 512 may implement any desired instruction set (e.g. Intel
Architecture-32 (IA-32, also known as x86), IA-32 with 64 bit
extensions, x86-64, PowerPC, Sparc, MIPS, ARM, IA-64, etc.). In
some embodiments, computer system 510 may include more than one
processor. Moreover, processor 512 may include one or more
processors or one or more processor cores.
Processor 512 may be coupled to memory 514 and peripheral devices
516 in any desired fashion. For example, in some embodiments,
processor 512 may be coupled to memory 514 and/or peripheral
devices 516 via various interconnect. Alternatively or in addition,
one or more bridge chips may be used to coupled processor 512,
memory 514, and peripheral devices 516.
Memory 514 may comprise any type of memory system. For example,
memory 514 may comprise DRAM, and more particularly double data
rate (DDR) SDRAM, RDRAM, etc. A memory controller may be included
to interface to memory 514, and/or processor 512 may include a
memory controller. Memory 514 may store the instructions to be
executed by processor 512 during use, data to be operated upon by
the processor during use, etc.
Peripheral devices 516 may represent any sort of hardware devices
that may be included in computer system 510 or coupled thereto
(e.g., storage devices, optionally including computer accessible
storage medium 600, shown in FIG. 9, other input/output (I/O)
devices such as video hardware, audio hardware, user interface
devices, networking hardware, etc.).
Turning now to FIG. 9, a block diagram of one embodiment of
computer accessible storage medium 600 including one or more data
structures representative of device 100 (depicted in FIG. 1)
included in an integrated circuit design and one or more code
sequences representative of process 200 and/or process 250 (shown
in FIGS. 4 and 7). Each code sequence may include one or more
instructions, which when executed by a processor in a computer,
implement the operations described for the corresponding code
sequence. Generally speaking, a computer accessible storage medium
may include any storage media accessible by a computer during use
to provide instructions and/or data to the computer. For example, a
computer accessible storage medium may include non-transitory
storage media such as magnetic or optical media, e.g., disk (fixed
or removable), tape, CD-ROM, DVD-ROM, CD-R, CD-RW, DVD-R, DVD-RW,
or Blu-Ray. Storage media may further include volatile or
non-volatile memory media such as RAM (e.g. synchronous dynamic RAM
(SDRAM), Rambus DRAM (RDRAM), static RAM (SRAM), etc.), ROM, or
Flash memory. The storage media may be physically included within
the computer to which the storage media provides instructions/data.
Alternatively, the storage media may be connected to the computer.
For example, the storage media may be connected to the computer
over a network or wireless link, such as network attached storage.
The storage media may be connected through a peripheral interface
such as the Universal Serial Bus (USB). Generally, computer
accessible storage medium 600 may store data in a non-transitory
manner, where non-transitory in this context may refer to not
transmitting the instructions/data on a signal. For example,
non-transitory storage may be volatile (and may lose the stored
instructions/data in response to a power down) or non-volatile.
Further modifications and alternative embodiments of various
aspects of the embodiments described in this disclosure will be
apparent to those skilled in the art in view of this description.
Accordingly, this description is to be construed as illustrative
only and is for the purpose of teaching those skilled in the art
the general manner of carrying out the embodiments. It is to be
understood that the forms of the embodiments shown and described
herein are to be taken as the presently preferred embodiments.
Elements and materials may be substituted for those illustrated and
described herein, parts and processes may be reversed, and certain
features of the embodiments may be utilized independently, all as
would be apparent to one skilled in the art after having the
benefit of this description. Changes may be made in the elements
described herein without departing from the spirit and scope of the
following claims.
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